Extending MASIVE: the impact of stress on imitation-based learning

In this paper we model the conjecture that stress has a negative impact on imitation-based learning, in the context of the IETAL and MASIVE theories. We pose a central question: Which is worse - a loss of available information due to stress, or the acquisition of misinformation due to stress? This question is addressed by modeling a modified IETAL agent in the MASIVE environment for which imitation-based learning is affected by stress. We present an algebraic representation of the modified agent, thus giving an extension of earlier work designed to explore learning phenomena in human subjects. Our goal is to break ground in a new direction in order to develop a richer theory based on our previous work in the domain of cognitive robotics and multiagent systems.